MarTech Interview with Brian Danzis, Chief Revenue Officer at Seedtag

Brian, could you describe your professional journey and how your background in analytics and project leadership has shaped your path to your current role?
The media and advertising landscape has evolved into a highly complex and fast-moving ecosystem. Leading in this environment requires a disciplined, data-driven mindset and the ability to quickly identify and act on key signals. With a diverse set of stakeholders—each with distinct goals—success hinges on being both analytically sharp and operationally agile. At Seedtag, our deep commitment to client centricity means we’re constantly listening, learning, and building. From innovative products to tailored insights and scalable processes, everything we do is designed to help our partners focus on what matters most—and deliver measurable impact.

What sets Seedtag’s definition of Contextual AI apart from legacy keyword or sentiment-based solutions?
Seedtag is entering a new era of contextual advertising with the unveiling of neuro-contextual understanding. Traditionally, contextual has been limited to content recognition based on URL analysis, keywords, and categories—often limiting campaigns to the upper funnel and predefined audience segments. Our AI, Liz, changes this paradigm. Grounded in neuroscience principles and network-level embeddings, Liz mirrors how the human brain processes content—reflecting these dynamics and matching ad placements to moments of strong interest, emotional receptivity, and purposeful engagement. This evolution allows us to offer dynamic, custom audiences, 360° campaign insights, and full-funnel, omnichannel outcomes—from awareness to action.

With neuro-contextual advertising, we are embarking on a new era—one where AI no longer just understands content, but shapes strategy, configures creatives, and drives results across the entire marketing funnel.

How do contextual embeddings improve ad relevance, and what makes them critical to Seedtag’s AI strategy?
Embeddings form the foundation of Seedtag’s neuro-contextual understanding. They convert content and campaign prompts into numerical vectors that represent meaning and intent. These vectors allow Liz to assess semantic alignment quickly and at scale—enabling full-funnel comprehension across screens.

By moving beyond simple classification, embeddings allow Liz to understand content in a human-like way—capturing signals such as interest, intent, and emotion. Neuroscience shows us that relevance is cognitive: familiar, context-aligned stimuli are easier to process and more likable, leading to increased attention and receptivity. By identifying emotionally charged intersections where users are most engaged, Liz boosts ad impact, enhances recall and brand affinity, and drives meaningful engagement.

How does Seedtag’s AI analyze and unify content across text, video, and audio for cross-channel targeting?
Liz uses network-level embeddings to extract semantic, emotional, and behavioral signals across multiple content formats like text, imagery, video, and audio. This allows Liz to interpret meaning holistically, going far beyond keyword or topic matching. Instead of simply placing ads near related content, Liz identifies where consumer engagement intersects with brand goals in real time—across CTV, video, and the open web. Operating like a human brain, Liz recognizes patterns, interprets intent, and responds dynamically. This results in smarter, more unified campaigns that deliver consistent and effective outcomes across channels.

What are the toughest contextual challenges in CTV, and how does the Beachfront acquisition help address them?
One major contextual challenge in CTV is that, by restricting targeting to broad genres, CTV platforms are blocking granular contextual targeting and many of the benefits that go along with granular targeting. Brand suitability is not possible if one does not know what content the ads are running in. Broad genre categories block the ability to do audience targeting without PII or IDs in a privacy-first manner. Reduced personalization or content relevance doesn’t allow brand messages to really resonate with audiences. Advertisers are forced to over-rely on household or demographic data, missing contextually rich placements that could improve brand recall and conversion rates.

The integration of Beachfront directly addresses these limitations. By elevating the quality of our CTV exchange with direct connections to premium broadcasters, we unlock higher-quality, contextually enriched inventory—both in open and managed service marketplaces. This gives our partners access to precise contextual targeting across CTV and the open web, without relying on personal identifiers—delivering the transparency, relevance, and control they need to run privacy-first, performance-driven campaigns.

Can contextual AI fully replace identity-based targeting in a cookieless world, and what evidence supports that?
The simple answer is yes. Especially with the introduction of neuro-contextual understanding, privacy-first targeting now has all the capabilities needed not only to compete with but often outperform identity-based targeting.

In a world where signal loss cuts out more than 50% of your audience, contextual is the only solution that provides full reach while respecting privacy. Traditional contextual strategies have excelled at upper-funnel awareness by aligning ads with the content people consume—but have historically fallen short in driving performance further down the funnel. That’s changing with neuro-contextual intelligence. Interpreting deeper signals like interest, emotion, and intent in real time, Liz can distinguish between casual browsing and purchase readiness. This unlocks tailored messaging and optimization across the entire marketing funnel. For example, in a campaign with a leading automotive brand in Europe, Seedtag lowered the cost per quality visit (CPQV) by an average of 68% below the target, while qualified visits tripled compared to the client’s original KPI. Neuro-contextual advertising isn’t just a privacy-first solution—it’s a viable, full-funnel alternative to identity-based strategies.

What does your AI infrastructure look like—from data ingestion to model deployment—at global scale?
Seedtag’s AI infrastructure is built around Liz, our neuro-contextual AI, and the Liz Agent, its activation interface. At the core is a powerful engine that ingests contextual signals—text, imagery, video, and audio—across omnichannel environments. Liz’s neuro-contextual understanding forms the brain of our system, interpreting complex content and behavioral patterns at scale. This intelligence powers the Liz Agent, our intuitive interface that dynamically builds audiences, optimizes delivery, and adjusts creative strategies in real time. Enhanced with large language model capabilities, the Liz Agent enables seamless interaction, making it easy for our clients to activate insights and streamline global campaign execution. Together, Liz and the Liz Agent form a fully integrated, privacy-first AI stack that delivers rapid responsiveness and full-funnel outcomes across the globe.

How does Seedtag balance contextual personalization with user privacy and regulatory compliance?
Seedtag has been built on a privacy-first foundation from the very beginning. Rather than tracking users or relying on personal identifiers, we’ve focused on understanding people through the lens of context—what they choose to engage with in the moment. Our approach leverages rich signals from text, imagery, video, and audio to interpret interest, emotion, and intent across the open web and CTV, without compromising personal data.

This method aligns seamlessly with global privacy regulations and public expectations for a more ethical digital experience. What someone chooses to view or read speaks volumes—not about who they are, but about what they care about in that moment. By focusing on the context, rather than the individual, we’re able to deliver personalization that feels intuitive and relevant, all while fully respecting user privacy and ensuring regulatory compliance.

What questions should advertisers ask to separate real AI-driven solutions from adtech hype?
To separate real AI-driven solutions from adtech hype, advertisers should ask the right questions. Was the technology developed in-house, or is it simply a white-labeled solution? What signals and datasets are the models trained on—and are they proprietary or publicly sourced? It’s also important to understand the type of AI or machine learning models being used, how they’re trained, and how frequently they’re updated. Advertisers should evaluate how the AI integrates into their existing tech stack and whether it solves meaningful workflow or decision-making challenges. Most importantly, is there measurable improvement in outcomes, and has that impact been independently validated? Finally, what KPIs does the AI optimize for, and how does the decisioning logic operate in practice? These questions help cut through the noise and identify solutions that truly add value.

What’s your advice to brands looking to future-proof their media strategy in an AI-driven, cookieless landscape?
One of the greatest challenges in bringing innovative media strategies to market is overcoming the instinct to evaluate them through the lens of outdated processes and legacy frameworks. True progress doesn’t just require a new strategy—it demands a new mindset.
If you’re serious about future-proofing your approach, my advice is this: when you commit to testing and learning, do so with intention. Remain flexible in how you execute, open to iteration, and hold off on judgment until you have results you can truly analyze.
See every decision through. Be adaptable in the details, but stay unwavering in your vision. That’s how meaningful innovation takes root—and drives lasting impact.